TL;DR: In this project, we collected and cataloged AI alignment research literature and analyzed the resulting dataset in an unbiased way to identify major research directions. We found that the field is growing quickly, with several subfields emerging in parallel. We looked at the subfields and identified the prominent researchers, recurring topics, and different modes of communication in each. Furthermore, we found that a classifier trained on AI alignment research articles can detect relevant articles that we did not originally include in the dataset. (video presentation here) Dataset Announcement In the context of the 6th AISC, we collected a dataset of alignment research articles from a variety of different sources. This dataset is now available for download here and the code for reproducing the scrape is on GitHub here[1]. When using the dataset, please cite our manuscript as described in the footnote[2]. Table 1: Different sources of text included in the dataset alongside the number of articles per source. Color of row indicates that data was analyzed as AI alignment research articles (green) or baseline (gray), or that the articles were added to the dataset as a result of the analysis in Fig. 4 (purple). Definition of level-0 and level-1 articles in Fig. 4c. For details about our collection procedure see the Methods section. Here follows an abbreviated version of the full manuscript, which contains additional analysis and discussion. Rapid growth of AI Alignment research from 2012 to 2022 across two platforms After collecting the dataset, we analyzed the two largest non-redundant sources of articles, Alignment Forum (AF) and arXiv. We found rapid growth in publications on the AF (Fig. 1a) and a long-tailed distribution of articles per researcher (Fig. 1b) and researchers per article (Fig. 1c). We were surprised to find a decrease in publications on the arXiv in recent years, but identified the cause for the decrease as spurious and fixed the issue in t